Overview

Dataset statistics

Number of variables13
Number of observations966
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.2 KiB
Average record size in memory104.1 B

Variable types

Numeric12
DateTime1

Alerts

df_index is highly correlated with relative_hourHigh correlation
memory_usage is highly correlated with gpu_memory_usage and 3 other fieldsHigh correlation
cpu_temp is highly correlated with gpu_tempHigh correlation
gpu_memory_usage is highly correlated with memory_usage and 3 other fieldsHigh correlation
gpu_load is highly correlated with memory_usage and 3 other fieldsHigh correlation
gpu_temp is highly correlated with memory_usage and 4 other fieldsHigh correlation
reported_hashrate is highly correlated with memory_usage and 3 other fieldsHigh correlation
relative_hour is highly correlated with df_indexHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
gpu_load has 311 (32.2%) zeros Zeros
reported_hashrate has 311 (32.2%) zeros Zeros

Reproduction

Analysis started2021-12-01 03:32:20.945050
Analysis finished2021-12-01 03:32:40.526127
Duration19.58 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean716.5
Minimum234
Maximum1199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:40.588865image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum234
5-th percentile282.25
Q1475.25
median716.5
Q3957.75
95-th percentile1150.75
Maximum1199
Range965
Interquartile range (IQR)482.5

Descriptive statistics

Standard deviation279.0044803
Coefficient of variation (CV)0.389399135
Kurtosis-1.2
Mean716.5
Median Absolute Deviation (MAD)241.5
Skewness0
Sum692139
Variance77843.5
MonotonicityNot monotonic
2021-11-30T22:32:40.728245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2341
 
0.1%
8381
 
0.1%
8701
 
0.1%
8711
 
0.1%
8721
 
0.1%
8731
 
0.1%
8741
 
0.1%
8751
 
0.1%
8761
 
0.1%
8771
 
0.1%
Other values (956)956
99.0%
ValueCountFrequency (%)
2341
0.1%
2351
0.1%
2361
0.1%
2371
0.1%
2381
0.1%
ValueCountFrequency (%)
11991
0.1%
11981
0.1%
11971
0.1%
11961
0.1%
11951
0.1%

ts
Date

Distinct866
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Minimum2021-11-10 09:10:41-05:00
Maximum2021-11-10 11:40:04-05:00
2021-11-30T22:32:40.866484image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:40.997677image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cpu_load
Real number (ℝ≥0)

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2085921325
Minimum0.1
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:41.102133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.2
Q30.2
95-th percentile0.6
Maximum1.4
Range1.3
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1398692406
Coefficient of variation (CV)0.6705393865
Kurtosis10.64219759
Mean0.2085921325
Median Absolute Deviation (MAD)0.1
Skewness2.681422072
Sum201.5
Variance0.01956340446
MonotonicityNot monotonic
2021-11-30T22:32:41.202243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.2421
43.6%
0.1348
36.0%
0.3117
 
12.1%
0.634
 
3.5%
0.414
 
1.4%
0.514
 
1.4%
0.711
 
1.1%
0.83
 
0.3%
12
 
0.2%
0.91
 
0.1%
ValueCountFrequency (%)
0.1348
36.0%
0.2421
43.6%
0.3117
 
12.1%
0.414
 
1.4%
0.514
 
1.4%
ValueCountFrequency (%)
1.41
 
0.1%
12
 
0.2%
0.91
 
0.1%
0.83
 
0.3%
0.711
1.1%

cpu_freq
Real number (ℝ≥0)

Distinct834
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean907.8632816
Minimum806.39
Maximum3593.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:41.336579image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum806.39
5-th percentile816.09
Q1833.9375
median859.27
Q3904.7175
95-th percentile1097.96
Maximum3593.58
Range2787.19
Interquartile range (IQR)70.78

Descriptive statistics

Standard deviation223.2820195
Coefficient of variation (CV)0.2459423396
Kurtosis71.55024177
Mean907.8632816
Median Absolute Deviation (MAD)32.62
Skewness7.698524868
Sum876995.93
Variance49854.86024
MonotonicityNot monotonic
2021-11-30T22:32:41.513954image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
848.14
 
0.4%
826.054
 
0.4%
845.884
 
0.4%
883.683
 
0.3%
851.323
 
0.3%
830.323
 
0.3%
833.153
 
0.3%
8193
 
0.3%
1097.963
 
0.3%
911.263
 
0.3%
Other values (824)933
96.6%
ValueCountFrequency (%)
806.391
0.1%
806.572
0.2%
807.482
0.2%
807.551
0.1%
808.231
0.1%
ValueCountFrequency (%)
3593.581
0.1%
3511.411
0.1%
3162.181
0.1%
2932.661
0.1%
2604.831
0.1%

memory_usage
Real number (ℝ≥0)

HIGH CORRELATION

Distinct734
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1398579925
Minimum1260351488
Maximum1467969536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:41.665410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1260351488
5-th percentile1261800448
Q11263621120
median1460670464
Q31464834048
95-th percentile1466445824
Maximum1467969536
Range207618048
Interquartile range (IQR)201212928

Descriptive statistics

Standard deviation93654378.03
Coefficient of variation (CV)0.06696390843
Kurtosis-1.420066876
Mean1398579925
Median Absolute Deviation (MAD)4802560
Skewness-0.7617102468
Sum1.351028208 × 1012
Variance8.771142524 × 1015
MonotonicityNot monotonic
2021-11-30T22:32:41.800670image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12629852165
 
0.5%
12623667204
 
0.4%
12640215044
 
0.4%
12621127684
 
0.4%
12621373444
 
0.4%
12614901764
 
0.4%
12636528644
 
0.4%
12634357764
 
0.4%
12623994884
 
0.4%
12631408644
 
0.4%
Other values (724)925
95.8%
ValueCountFrequency (%)
12603514881
0.1%
12604170241
0.1%
12605767681
0.1%
12605808641
0.1%
12606423042
0.2%
ValueCountFrequency (%)
14679695361
0.1%
14679367681
0.1%
14677729281
0.1%
14675681281
0.1%
14675271681
0.1%

cpu_temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.72877847
Minimum29
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:41.915536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile30
Q131
median35
Q335
95-th percentile36
Maximum37
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.253595851
Coefficient of variation (CV)0.06681522287
Kurtosis-0.7720224253
Mean33.72877847
Median Absolute Deviation (MAD)1
Skewness-0.8186455505
Sum32582
Variance5.078694258
MonotonicityNot monotonic
2021-11-30T22:32:42.095599image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
35376
38.9%
36156
16.1%
34121
 
12.5%
30119
 
12.3%
3187
 
9.0%
2940
 
4.1%
3326
 
2.7%
3222
 
2.3%
3719
 
2.0%
ValueCountFrequency (%)
2940
 
4.1%
30119
12.3%
3187
9.0%
3222
 
2.3%
3326
 
2.7%
ValueCountFrequency (%)
3719
 
2.0%
36156
16.1%
35376
38.9%
34121
 
12.5%
3326
 
2.7%

gpu_memory_usage
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2283186316
Minimum9437184
Maximum3362783232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:42.195506image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9437184
5-th percentile9437184
Q19437184
median3362783232
Q33362783232
95-th percentile3362783232
Maximum3362783232
Range3353346048
Interquartile range (IQR)3353346048

Descriptive statistics

Standard deviation1567570195
Coefficient of variation (CV)0.6865712989
Kurtosis-1.420217133
Mean2283186316
Median Absolute Deviation (MAD)0
Skewness-0.7633659495
Sum2.205557981 × 1012
Variance2.457276315 × 1018
MonotonicityDecreasing
2021-11-30T22:32:42.288501image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
3362783232655
67.8%
9437184311
32.2%
ValueCountFrequency (%)
9437184311
32.2%
3362783232655
67.8%
ValueCountFrequency (%)
3362783232655
67.8%
9437184311
32.2%

gpu_load
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.80331263
Minimum0
Maximum100
Zeros311
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:42.386478image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation46.74504356
Coefficient of variation (CV)0.6894212355
Kurtosis-1.420217485
Mean67.80331263
Median Absolute Deviation (MAD)0
Skewness-0.7633629292
Sum65498
Variance2185.099098
MonotonicityNot monotonic
2021-11-30T22:32:42.471996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
100653
67.6%
0311
32.2%
992
 
0.2%
ValueCountFrequency (%)
0311
32.2%
992
 
0.2%
100653
67.6%
ValueCountFrequency (%)
100653
67.6%
992
 
0.2%
0311
32.2%

gpu_temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct34
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.13768116
Minimum32
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:42.568457image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile32
Q134
median76
Q377
95-th percentile78
Maximum78
Range46
Interquartile range (IQR)43

Descriptive statistics

Standard deviation19.95945861
Coefficient of variation (CV)0.3161259369
Kurtosis-1.295335885
Mean63.13768116
Median Absolute Deviation (MAD)1
Skewness-0.817234516
Sum60991
Variance398.379988
MonotonicityNot monotonic
2021-11-30T22:32:42.686755image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
77361
37.4%
76222
23.0%
32144
 
14.9%
3384
 
8.7%
7856
 
5.8%
3420
 
2.1%
7516
 
1.7%
359
 
0.9%
369
 
0.9%
375
 
0.5%
Other values (24)40
 
4.1%
ValueCountFrequency (%)
32144
14.9%
3384
8.7%
3420
 
2.1%
359
 
0.9%
369
 
0.9%
ValueCountFrequency (%)
7856
 
5.8%
77361
37.4%
76222
23.0%
7516
 
1.7%
711
 
0.1%

hashrate
Real number (ℝ≥0)

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005543.523
Minimum119772.51
Maximum1676815.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:42.788655image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum119772.51
5-th percentile119772.51
Q1598862.54
median958180.07
Q31557042.61
95-th percentile1676815.12
Maximum1676815.12
Range1557042.61
Interquartile range (IQR)958180.07

Descriptive statistics

Standard deviation513635.7324
Coefficient of variation (CV)0.5108040781
Kurtosis-1.329616308
Mean1005543.523
Median Absolute Deviation (MAD)479090.04
Skewness0.02612863759
Sum971355043.6
Variance2.638216656 × 1011
MonotonicityNot monotonic
2021-11-30T22:32:42.878448image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1676815.12228
23.6%
958180.07114
11.8%
479090.03112
11.6%
718635.0598
10.1%
119772.5160
 
6.2%
359317.5360
 
6.2%
838407.5660
 
6.2%
1437270.160
 
6.2%
1557042.6160
 
6.2%
1077952.5760
 
6.2%
ValueCountFrequency (%)
119772.5160
6.2%
359317.5360
6.2%
479090.03112
11.6%
598862.5454
5.6%
718635.0598
10.1%
ValueCountFrequency (%)
1676815.12228
23.6%
1557042.6160
 
6.2%
1437270.160
 
6.2%
1077952.5760
 
6.2%
958180.07114
11.8%

unpaid
Real number (ℝ≥0)

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127975454.6
Minimum127800788
Maximum129278234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:42.971565image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum127800788
5-th percentile127800788
Q1127800788
median127800788
Q3127891181
95-th percentile128644815
Maximum129278234
Range1477446
Interquartile range (IQR)90393

Descriptive statistics

Standard deviation362543.2666
Coefficient of variation (CV)0.002832912513
Kurtosis4.791954462
Mean127975454.6
Median Absolute Deviation (MAD)0
Skewness2.318439329
Sum1.236242891 × 1011
Variance1.314376202 × 1011
MonotonicityIncreasing
2021-11-30T22:32:43.057846image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
127800788672
69.6%
12819234062
 
6.4%
12864481560
 
6.2%
12837330748
 
5.0%
12927823440
 
4.1%
12783093330
 
3.1%
12789118130
 
3.1%
12786105812
 
1.2%
12804178312
 
1.2%
ValueCountFrequency (%)
127800788672
69.6%
12783093330
 
3.1%
12786105812
 
1.2%
12789118130
 
3.1%
12804178312
 
1.2%
ValueCountFrequency (%)
12927823440
4.1%
12864481560
6.2%
12837330748
5.0%
12819234062
6.4%
12804178312
 
1.2%

reported_hashrate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean847541.4079
Minimum0
Maximum1250000
Zeros311
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:43.148128image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1250000
Q31250000
95-th percentile1250000
Maximum1250000
Range1250000
Interquartile range (IQR)1250000

Descriptive statistics

Standard deviation584313.0446
Coefficient of variation (CV)0.6894212355
Kurtosis-1.420217485
Mean847541.4079
Median Absolute Deviation (MAD)0
Skewness-0.7633629292
Sum818725000
Variance3.41421734 × 1011
MonotonicityNot monotonic
2021-11-30T22:32:43.234879image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1250000653
67.6%
0311
32.2%
12375002
 
0.2%
ValueCountFrequency (%)
0311
32.2%
12375002
 
0.2%
1250000653
67.6%
ValueCountFrequency (%)
1250000653
67.6%
12375002
 
0.2%
0311
32.2%

relative_hour
Real number (ℝ≥0)

HIGH CORRELATION

Distinct866
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0318438
Minimum0.6725
Maximum3.162222222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2021-11-30T22:32:43.346024image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.6725
5-th percentile0.811875
Q11.367361111
median2.061527778
Q32.756180556
95-th percentile3.093333333
Maximum3.162222222
Range2.489722222
Interquartile range (IQR)1.388819444

Descriptive statistics

Standard deviation0.7603161449
Coefficient of variation (CV)0.3742000959
Kurtosis-1.286576485
Mean2.0318438
Median Absolute Deviation (MAD)0.6951388889
Skewness-0.1327714634
Sum1962.761111
Variance0.5780806402
MonotonicityIncreasing
2021-11-30T22:32:43.478417image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1622222222
 
0.2%
2.9811111112
 
0.2%
2.9522222222
 
0.2%
2.9552777782
 
0.2%
2.9580555562
 
0.2%
2.9608333332
 
0.2%
2.9638888892
 
0.2%
2.9666666672
 
0.2%
2.9694444442
 
0.2%
2.97252
 
0.2%
Other values (856)946
97.9%
ValueCountFrequency (%)
0.67251
0.1%
0.67555555561
0.1%
0.67833333331
0.1%
0.68111111111
0.1%
0.68416666671
0.1%
ValueCountFrequency (%)
3.1622222222
0.2%
3.1591666672
0.2%
3.1563888892
0.2%
3.1536111112
0.2%
3.1508333332
0.2%

Interactions

2021-11-30T22:32:38.503603image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:21.281832image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:22.687775image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:24.292755image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:25.841577image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:27.303857image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:28.976351image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:30.635908image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2021-11-30T22:32:43.593042image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-30T22:32:43.863232image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-30T22:32:44.110863image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-30T22:32:44.306588image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-30T22:32:40.237134image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-30T22:32:40.453306image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
02342021-11-10 09:10:41-05:000.4885.92146153881633.03.362783e+09100.076.0119772.511278007881250000.00.672500
12352021-11-10 09:10:52-05:000.2852.95146090803234.03.362783e+09100.076.0119772.511278007881250000.00.675556
22362021-11-10 09:11:02-05:000.21573.96146086707234.03.362783e+09100.076.0119772.511278007881250000.00.678333
32372021-11-10 09:11:12-05:000.2904.47145993318434.03.362783e+09100.076.0119772.511278007881250000.00.681111
42382021-11-10 09:11:23-05:000.1834.99145934336034.03.362783e+09100.076.0119772.511278007881250000.00.684167
52392021-11-10 09:11:33-05:000.3845.68145940070435.03.362783e+09100.076.0119772.511278007881250000.00.686944
62402021-11-10 09:11:43-05:000.2864.41146015027233.03.362783e+09100.076.0119772.511278007881250000.00.689722
72412021-11-10 09:11:54-05:000.1852.13146107596834.03.362783e+09100.076.0119772.511278007881250000.00.692778
82422021-11-10 09:12:04-05:000.2863.66146114969634.03.362783e+09100.076.0119772.511278007881250000.00.695556
92432021-11-10 09:12:15-05:000.31098.32145987584033.03.362783e+09100.076.0119772.511278007881250000.00.698611

Last rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
95610952021-11-10 11:39:23-05:000.1818.64126346444829.09437184.00.032.0479090.031292782340.03.150833
95711952021-11-10 11:39:23-05:000.1818.64126346444829.09437184.00.032.0479090.031292782340.03.150833
95811962021-11-10 11:39:33-05:000.1969.61126298521631.09437184.00.032.0479090.031292782340.03.153611
95910962021-11-10 11:39:33-05:000.1969.61126298521631.09437184.00.032.0479090.031292782340.03.153611
96010972021-11-10 11:39:43-05:000.1816.44126298521629.09437184.00.032.0479090.031292782340.03.156389
96111972021-11-10 11:39:43-05:000.1816.44126298521629.09437184.00.032.0479090.031292782340.03.156389
96210982021-11-10 11:39:53-05:000.1830.32126250188830.09437184.00.032.0479090.031292782340.03.159167
96311982021-11-10 11:39:53-05:000.1830.32126250188830.09437184.00.032.0479090.031292782340.03.159167
96410992021-11-10 11:40:04-05:000.1815.89126271488030.09437184.00.032.0479090.031292782340.03.162222
96511992021-11-10 11:40:04-05:000.1815.89126271488030.09437184.00.032.0479090.031292782340.03.162222